Clinical validation studies of neoral C2 monitoring: a review

2002 ◽  
Vol 73 (Supplement) ◽  
pp. S3-S11 ◽  
Author(s):  
Bj??rn Nashan ◽  
Edward Cole ◽  
Gary Levy ◽  
Eric Thervet
Keyword(s):  
Validation Studies ◽  
Clinical Validation ◽  
C2 Monitoring

scholarly journals Qualification of translational safety biomarkers

2021 ◽  
pp. 153537022110021
Author(s):  
John-Michael Sauer ◽  
Amy C Porter
Keyword(s):  
Skeletal Muscle ◽  
Drug Development ◽  
Vascular Injury ◽  
Validation Studies ◽  
Safety Assessment ◽  
Regulatory Agencies ◽  
Development Studies ◽  
Clinical Validation ◽  
Development Tools ◽  
Important Drug

Safety biomarkers are important drug development tools, both preclinically and clinically. It is a straightforward process to correlate the performance of nonclinical safety biomarkers with histopathology, and ideally, the biomarker is useful in all species commonly used in safety assessment. In clinical validation studies, where histopathology is not feasible, safety biomarkers are compared to the response of standard biomarkers and/or to clinical adjudication. Worldwide, regulatory agencies have put in place processes to qualify biomarkers to provide confidence in the manner of use and interpretation of biomarker data in drug development studies. This paper describes currently qualified safety biomarkers which can be utilized to monitor for nephrotoxicity and cardiotoxicity and ongoing projects to qualify safety biomarkers for liver, skeletal muscle, and vascular injury. In many cases, the development and use of these critical drug development tools is dependent upon partnerships and the precompetitive sharing of data to support qualification efforts.

scholarly journals Multiplexed triage of candidate biomarkers in plasma using internal standard triggered-parallel reaction monitoring mass spectrometry

2021 ◽  
Author(s):  
Jacob J Kennedy ◽  
Jeffrey R Whiteaker ◽  
Richard G Ivey ◽  
Aura Burian ◽  
Shrabanti Chowdhury ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Validation Studies ◽  
Internal Standard ◽  
Clinical Validation ◽  
Reaction Monitoring ◽  
Parallel Reaction ◽  
Large Numbers ◽  
Parallel Reaction Monitoring ◽  
Major Bottleneck ◽  
Using Data

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

scholarly journals Sample Size Analysis for Machine Learning Clinical Validation Studies

2021 ◽  
Author(s):  
Daniel M. Goldenholz ◽  
Haoqi Sun ◽  
Wolfgang Ganglberger ◽  
M. Brandon Westover
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Validation Studies ◽  
Performance Metrics ◽  
Receiver Operating Curve ◽  
Size Analysis ◽  
Clinical Validation ◽  
Sample Sizes ◽  
Precision And Accuracy ◽  
Minimum Sample

ABSTRACTOBJECTIVEBefore integrating new machine learning (ML) into clinical practice, algorithms must undergo validation. Validation studies require sample size estimates. Unlike hypothesis testing studies seeking a p-value, the goal of validating predictive models is obtaining estimates of model performance. Our aim was to provide a standardized, data distribution- and model-agnostic approach to sample size calculations for validation studies of predictive ML models.MATERIALS AND METHODSSample Size Analysis for Machine Learning (SSAML) was tested in three previously published models: brain age to predict mortality (Cox Proportional Hazard), COVID hospitalization risk prediction (ordinal regression), and seizure risk forecasting (deep learning). The SSAML steps are: 1) Specify performance metrics for model discrimination and calibration. For discrimination, we use area under the receiver operating curve (AUC) for classification and Harrell’s C-statistic for survival models. For calibration, we employ calibration slope and calibration-in-the-large. 2) Specify the required precision and accuracy (≤0.5 normalized confidence interval width and ±5% accuracy). 3) Specify the required coverage probability (95%). 4) For increasing sample sizes, calculate the expected precision and bias that is achievable. 5) Choose the minimum sample size that meets all requirements.RESULTSMinimum sample sizes were obtained in each dataset using standardized criteria.DISCUSSIONSSAML provides a formal expectation of precision and accuracy at a desired confidence level.CONCLUSIONSSAML is open-source and agnostics to data type and ML model. It can be used for clinical validation studies of ML models.

Quality appraisal of clinical validation studies for multigene prediction assays of chemotherapy response in early-stage breast cancer.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 3082-3082 ◽  
Author(s):  
N. M. Kuderer ◽  
E. Culakova ◽  
M. Huang ◽  
M. S. Poniewierski ◽  
G. S. Ginsburg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Validation Studies ◽  
Early Stage ◽  
Early Stage Breast Cancer ◽  
Chemotherapy Response ◽  
Clinical Validation ◽  
Quality Appraisal

scholarly journals Patient-specific Meta-analysis of 2 Clinical Validation Studies to Predict Pathologic Outcomes in Prostate Cancer Using the 17-Gene Genomic Prostate Score

Urology ◽  
2016 ◽  
Vol 89 ◽  
pp. 69-75 ◽  
Author(s):  
Timothy C. Brand ◽  
Nan Zhang ◽  
Michael R. Crager ◽  
Tara Maddala ◽  
Anne Dee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Studies ◽  
Meta Analysis ◽  
Patient Specific ◽  
Clinical Validation

scholarly journals How precise is “precision” of hemodynamic measurements in clinical validation studies?

2014 ◽  
Vol 40 (7) ◽  
pp. 1064-1064 ◽  
Author(s):  
Martin Petzoldt ◽  
Bernd Saugel ◽  
Daniel A. Reuter
Keyword(s):  
Validation Studies ◽  
Clinical Validation ◽  
Hemodynamic Measurements

Clinical validation studies on clinical workstation prototypes

1999 ◽  
Author(s):  
Bert Verdonck ◽  
Karel C. Strasters ◽  
Frans A. Gerritsen
Keyword(s):  
Validation Studies ◽  
Clinical Validation ◽  
Clinical Workstation

scholarly journals Integrating Physiology into the DNA of Coronary Revascularisation – A Historical Perspective, Contemporary Review and Blueprint for the Future of Coronary Physiology

2015 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Sen Sayan ◽  
Davies Justin ◽  
◽  
Keyword(s):  
Validation Studies ◽  
Historical Perspective ◽  
Economic Benefits ◽  
Coronary Revascularisation ◽  
Clinical Validation ◽  
Fractional Flow ◽  
Physiological Basis ◽  
Flow Reserve ◽  
Stenosis Severity ◽  
Clinical Adoption

The clinical and economic benefits of physiologically guided revascularisation have been demonstrated, yet its clinical adoption remains unacceptably low. Recently, new indices of stenosis severity have been introduced that aim to improve adoption by circumventing the limitations of existing indices. The most validated of these new indices is the instantaneous wave-free ratio (iFR). This review will describe the physiological basis of this index, how it avoids the problems of existing indices such as fractional flow reserve (FFR) and the clinical validation studies of iFR to date. We will then describe a novel use of iFR, which has the potential to transform the use of physiology in the catheter lab and finally integrate physiology into the DNA of coronary revascularisation.

Sign in / Sign up

Export Citation Format

Share Document